Signal and information processing for sensing systems

Santiago Marco Colás | Group Leader
Agustín Gutiérrez Gálvez | Senior Researcher
Raquel Cumeras Olmeda | Postdoctoral Researcher
Jordi Fonollosa Magrinyà | Postdoctoral Researcher
Silvia Mas Garcia | Postdoctoral Researcher
Jia Yan | Postdoctoral Researcher
Javier Burgués Calderón | PhD Student
Ana Maria Solórzano Soria | PhD Student
Lluís Fernández Romero | Laboratory Technician
Saeedeh Taghadomi-Saberi | Visiting Researcher


santi2015_1Current smart instrumentation using multi-sensors and/or spectrometers provides a wealth of data that requires sophisticated signal and data processing approaches in order to extract the hidden information.

Left: System to test chemical sensor arrays for diversity and redundancy

In this context, we are interested in intelligent chemical instruments for the detection of volatile compounds and smells.

These systems can be based on an array of nonspecific chemical sensors with a pattern recognition system , taking inspiration from the olfactory system. Some spectrometries, e.g. Ion Mobility Spectrometry, are capable of very fast analysis with good detection limits but poor selectivity. These technologies have been proposed for the fast determination of the volatolome (volatile fraction of the metabolome), instead of the reference technique of gas chromatography – mass spectrometry.

santi2015_2Our group develops algorithmic solutions for the automatic processing of Gas Sensor Array, Ion Mobility Spectrometry (IMS) and Gas Chromatography – Mass Spectrometry (GC-MS) data for metabolomics and food samples.

In a parallel activity, our group is working on the detection of drowsiness in drivers using vehicle dynamic measures.

Right: System to test chemical chemical sensors for malodour detection

Our research in 2016 included the following:

Signal and Data Processing for smart chemical Instrumentation:
1. We have studied calibration transfer methods among chemical sensor arrays to reduce calibration costs.
2. We have studied how to reduce the cross-sensitivity to humidity in chemical sensors
3. We have designed methodologies to estimate the limit of detection in chemical sensors inspired in the IUPAC recommendations.
4. We have been working in the detection of toxic emissions from fires to improve building occupant’s safety.
5. In collaboration with Universitat de Lleida (Dr. J. Palacin) and University of Örebro (Prof. A. Lilienthal) we are testing chemical source localization algorithms with autonomous robots.


Left: The SAFESENS project aims to produce personal health monitors for emergency personnel including toxic hazards detection


IBEC/UB team to take part in battle of artificial noses

Two undergraduate biomedical engineering students who are doing their practical work at IBEC have been selected to go to Montreal to compete in SNIFFEST, a new international competition promoting practical and original solutions for odour measurement.

PIONER award for IBEC student

Former IBEC PhD student Ariadna Bartra has been awarded a Premi PIONER from CERCA.

Three minutes of fame

IBEC PhD student Ana Solorzano has been selected as one of the 10 finalists from 300 participants in a “Thesis in 3 Minutes” competition that will take place at the 2016 Jornadas de Cooperación CONACyT-Catalunya (JCCC) next week.

How we learn in chunks – and what it means for the brain

In some research conducted with his colleagues at the University of California San Diego, IBEC senior researcher Jordi Fonollosa has shed some light on the mechanisms behind how we memorize sequences – as well as how failures in these mechanisms can provide insight into neurological disorders.

Olfactory system segregates odour data at moment of input

We all know how emotive smells can be; just the mere whiff of candy floss or plasticine can transport you back decades in a moment. But how does our olfactory system recognise odours even at very low doses? Now, IBEC’s Signal and Information Processing for Sensing Systems group has uncovered how we immediately and strongly identify smells even when their concentrations are very low, essentially segregating information about identification and concentration in order to be able to recognise the odour.

“Buenos días, robot”

IBEC group leaders Alícia Casals and Santiago Marco feature in a Sunday supplement about robots in La Vanguardia this week.

Is the poison/explosive/biomarker really there?

A paper by IBEC’s Signal and Information Processing for Sensing Systems group and their collaborators is the featured article and appears on the cover of the latest issue of Analytica Chimica Acta.

”Nova aplicació mòbil que detecta la somnolència al volant”

Santiago Marco is interviewed in a video for UBTV about Somnoalert®, the mobile technology his group developed with the UB and industry partner FICOSA to detect drowsiness while driving.

“El laboratori al ‘Connexió’: El nas electrònic del IBEC”

PhD student Sergio Oller from the Signal and Information Processing for Sensing Systems talking about the group’s research into the development of electronic noses based on the mammalian olfactory system.

“Una aplicación detecta la somnolencia al volante”

Further coverage of the driver drowsiness alerter Somnoalert® developed by IBEC and Ficosa has appeared today in El Mundo and

“Ficosa, IBEC y la UPC crean un aplicación para detectar la somnolencia al volante”

Monday’s press release about the driver drowsiness alerter Somnoalert® developed by IBEC and Ficosa got lots of coverage in the news this week, including in La Vanguardia, TeleCinco and El Economista.

IBEC, UB and FICOSA join forces to develop a mobile application to detect drowsiness while driving

A new technology to combat dozing off when driving developed by IBEC, UB and industry partner Ficosa will be presented at this week’s GSMA World Mobile Congress in Barcelona.

A new name for the AO group

Santiago Marco’s research group at IBEC has changed its name and will now be known as the Signal and Information Processing for Sensing Systems group.

How the nose knows

The mammalian sense of smell is an excellent chemical sensing system that far outshines any man-made reproduction, so researchers have long been trying to analyze and recreate the animal olfactory system to develop artificial ‘noses’. Now researchers at IBEC have shed new light on this highly efficient system that could allow better chemical sensing systems with important applications in such critical areas as health, security or the food industry.

Santiago Marco elected president of ISOCS

At the General Assembly of the International Society of Olfaction and Chemical Sensing at Rockefeller University, New York, Artificial Olfaction group leader Santiago Marco was elected President of the society for a period of two years.

Neurochem’s Workshop on Bioinspired Computation for Chemical Sensing

Last week the University of Barcelona, as a coordinator of the EU-FP7 Neurochem Project, joined with IBEC to organize the Workshop on Bioinspired Computation for Chemical Sensing, which was held on 9-11 March at the Hotel Senator in Barcelona. The focus of the workshop was on signal processing techniques inspired by the olfactory system and computational models of the biological olfactory pathway.

Smelling by minispectrometer provides fast determination of wine origin

Wine fraud is a growing problem, with experts estimating that up to 10% of the wines offered to consumers in some European countries are of a lesser quality than the label claims. It’s an issue that affects everyone from expert collectors to average consumers, and is such a concern in some countries that drastic measures have been taken: the Italian Carabinieri Corps, for instance, has educated 25 of their officers as sommeliers.

IBEC hosts Summer School 2009 of International Society for Olfaction and Chemical Sensing

The research team of Artificial Olfaction at IBEC, led by Santiago Marco, organized the Summer School of the International Society for Olfaction and Chemical Sensing (ISOCS). It was held from September 28th, to October 2nd of 2009, at Sant Andreu de Llavaneres, Barcelona, Spain. This Summer School was supported by the FP7 Neurochem European project, coordinated by the IBEC’s research team.


EU-funded projects
SAFESENS Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants (2014-2017) ENIAC Joint Undertaking Santiago Marco
National projects
SMART-IMS Procesado de Señal para Espectroscopia de Movilidad de Iones: Análisis de Fluidos Biomédicos y Detección de Sustancias Tóxicas (2012-2015) MINECO, I+D-Investigación fundamental no orientada Santiago Marco
Transducción biomimética para olfacción artificial MINECO, EUROPA EXCELENCIA Agustín Gutiérrez
BIOENCODE Estudio comparativo de la capacidad de codificación de información química de sistemas biológicos y artificiales MINECO, I+D-Investigación fundamental no orientada Agustín Gutiérrez
SENSIBLE Sensores inteligentes para edificios más seguros (2014-2016) MINECO, Acciones de Programación Conjunta Internacional Santiago Marco
SIGVOL Mejora de la señal para instrumentación química: aplicaciones en metabolómica de volátiles y en olfacción (2015-2017) MINECO, Retos investigación: Proyectos I+D Santiago Marco
Privately-funded projects
Analisis de tapones de corcho por espectroscopia de movilidad de iones (2015-2016) M3C INDUSTRIAL AUTOMATION & VISION, S.L. Santiago Marco
Sensor test for indoor air quality and safety applications (2015-2016) BSH Electrodomesticos España S.A. Santiago Marco
Preparació i realització d’un curs de processat de senyal per sensors químics de dos dies a BSH Zaragoza (2016-2017) BSH Electrodomesticos España S.A. Santiago Marco


Moreno, Sergio Oller, Cominetti, Ornella, Galindo, Antonio Núñez, Irincheeva, Irina, Corthésy, John, Astrup, Arne, Saris, Wim H. M., Hager, Jörg, Kussmann, Martin, Dayon, Loïc, (2017). The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention PROTEOMICS - Clinical Applications Early View (Online Version of Record published before inclusion in an issue), Accepted Article

Purpose : The nutritional intervention program “DiOGenes” focuses on how obesity can be prevented and treated from a dietary perspective. We generated differential plasma proteome profiles in the DiOGenes cohort to identify proteins associated with weight loss and maintenance and explore their relation to body mass index, fat mass, insulin resistance and sensitivity. Experimental Design : Relative protein quantification was obtained at baseline and after combined weight loss/maintenance phases using isobaric tagging and MS/MS. A Welch t-test determined proteins differentially present after intervention. Protein relationships with clinical variables were explored using univariate linear models, considering collection center, gender and age as confounding factors. Results : 473 subjects were measured at baseline and end of the intervention; 39 proteins were longitudinally differential. Proteins with largest changes were sex hormone-binding globulin, adiponectin, C-reactive protein, calprotectin, serum amyloid A, and proteoglycan 4 (PRG4), whose association with obesity and weight loss is known. We identified new putative biomarkers for weight loss/maintenance. Correlation between PRG4 and proline-rich acidic protein 1 (PRAP1) variation and Matsuda insulin sensitivity increment was showed. Conclusions and Clinical Relevance : MS-based proteomic analysis of a large cohort of non-diabetic overweight and obese individuals concomitantly identified known and novel proteins associated with weight loss and maintenance.

Keywords: Biomarker, Diabetes, Large-scale study, Mass spectrometry, Obesity, Proteomics

Pomareda, V., Magrans, R., Jiménez-Soto, J., Martínez, D., Tresánchez, M., Burgués, J., Palacín, J., Marco, S., (2017). Chemical source localization fusing concentration information in the presence of chemical background noise Sensors 17, (4), 904

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented.

Keywords: Machine olfaction, Odor robots, Chemical sensors, Bayesian inference

Fonollosa, J., Fernández, L., Gutiérrez-Gálvez, A., Huerta, R., Marco, S., (2016). Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization Sensors and Actuators B: Chemical 236, 1044-1053

Inherent variability of chemical sensors makes it necessary to calibrate chemical detection systems individually. This shortcoming has traditionally limited usability of systems based on metal oxide gas sensor arrays and prevented mass-production for some applications. Here, aiming at exploring calibration transfer between chemical sensor arrays, we exposed five twin 8-sensor detection units to different concentration levels of ethanol, ethylene, carbon monoxide, or methane. First, we built calibration models using data acquired with a master unit. Second, to explore the transferability of the calibration models, we used Direct Standardization to map the signals of a slave unit to the space of the master unit in calibration. In particular, we evaluated the transferability of the calibration models to other detection units, and within the same unit measuring days apart. Our results show that signals acquired with one unit can be successfully mapped to the space of a reference unit. Hence, calibration models trained with a master unit can be extended to slave units using a reduced number of transfer samples, diminishing thereby calibration costs. Similarly, signals of a sensing unit can be transformed to match sensor behavior in the past to mitigate drift effects. Therefore, the proposed methodology can reduce calibration costs in mass-production and delay recalibrations due to sensor aging. Acquired dataset is made publicly available.

Keywords: Calibration transfer, Chemical sensors, Direct Standardization, Electronic nose, MOX sensors, Public dataset

Fernandez, L., Guney, S., Gutierrez-Galvez, A., Marco, S., (2016). Calibration transfer in temperature modulated gas sensor arrays Sensors and Actuators B: Chemical 231, 276-284

Abstract Shifts in working temperature are an important issue that prevents the successful transfer of calibration models from one chemical instrument to another. This effect is of special relevance when working with gas sensor arrays modulated in temperature. In this paper, we study the use of multivariate techniques to transfer the calibration model from a temperature modulated gas sensor array to another when a global change of temperature occurs. To do so, we built 12 identical master sensor arrays composed of three different types of commercial Figaro sensors and acquired a dataset of sensor responses to three pure substances (ethanol, acetone and butanone) dosed at 7 concentrations. The master arrays are then shifted in temperature (from −50 to 50 °C, ΔT = 10 °C) and considered as slave arrays. Data correction is performed for an increasing number of transfer samples with 4 different calibration transfer techniques: Direct Standardization, Piece-wise Direct Standardization, Orthogonal Signal Correction and Generalized Least Squares Weighting. In order to evaluate the performance of the calibration transfer, we compare the Root Mean Square Error of Prediction (RMSEP) of master and slave arrays, for each instrument correction. Best results are obtained from Piece-wise Direct standardization, which exhibits the lower RMSEP values after correction for the smaller number of transfer samples.

Keywords: Calibration transfer, Gas sensor array, MOX, Temperature modulation

Huerta, R., Mosqueiro, T., Fonollosa, J., Rulkov, N.F., Rodríguez-Lujan, I., (2016). Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring Chemometrics and Intelligent Laboratory Systems 157, 169-176

A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor the air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 sample per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors' signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination R2 close to 1. To show how the humidity–temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors.

Keywords: Electronic nose, Chemical sensors, Humidity, Temperature, Decorrelation, Wireless e-nose, MOX sensors, Energy band model, Home monitoring

Martínez, D., Moreno, J., Tresanchez, M., Clotet, E., Jiménez-Soto, J.M., Magrans, R., Pardo, A., Marco, S., Palacín, J., (2016). Measuring gas concentration and wind intensity in a turbulent wind tunnel with a mobile robot Journal of Sensors 2016, Article ID 7184980

This paper presents the measurement of gas concentration and wind intensity performed with a mobile robot in a custom turbulent wind tunnel designed for experimentation with customizable wind and gas leak sources. This paper presents the representation in different information layers of the measurements obtained in the turbulent wind tunnel under different controlled environmental conditions in order to describe the plume of the gas and wind intensities inside the experimentation chamber. The information layers have been generated from the measurements gathered by individual onboard gas and wind sensors carried out by an autonomous mobile robot. On the one hand, the assumption was that the size and cost of these specialized sensors do not allow the creation of a net of sensors or other measurement alternatives based on the simultaneous use of several sensors, and on the other hand, the assumption is that the information layers created will have application on the development and test of automatic gas source location procedures based on reactive or nonreactive algorithms.

Ziyatdinov, Andrey, Fonollosa, Jordi, Fernández, Luis, Gutiérrez-Gálvez, Agustín, Marco, Santiago, Perera, Alexandre, (2015). Data set from gas sensor array under flow modulation Data in Brief 3, 131-136

Abstract Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.

Keywords: Gas sensor array, MOX sensor, Flow modulation, Early detection, Biomimetics, Respiration, Sniffing

Ziyatdinov, Andrey, Fonollosa, Jordi, Fernánndez, Luis, Gutierrez-Gálvez, Agustín, Marco, Santiago, Perera, Alexandre, (2015). Bioinspired early detection through gas flow modulation in chemo-sensory systems Sensors and Actuators B: Chemical 206, 538-547

Abstract The design of bioinspired systems for chemical sensing is an engaging line of research in machine olfaction. Developments in this line could increase the lifetime and sensitivity of artificial chemo-sensory systems. Such approach is based on the sensory systems known in live organisms, and the resulting developed artificial systems are targeted to reproduce the biological mechanisms to some extent. Sniffing behaviour, sampling odours actively, has been studied recently in neuroscience, and it has been suggested that the respiration frequency is an important parameter of the olfactory system, since the odour perception, especially in complex scenarios such as novel odourants exploration, depends on both the stimulus identity and the sampling method. In this work we propose a chemical sensing system based on an array of 16 metal-oxide gas sensors that we combined with an external mechanical ventilator to simulate the biological respiration cycle. The tested gas classes formed a relatively broad combination of two analytes, acetone and ethanol, in binary mixtures. Two sets of low-frequency and high-frequency features were extracted from the acquired signals to show that the high-frequency features contain information related to the gas class. In addition, such information is available at early stages of the measurement, which could make the technique suitable in early detection scenarios. The full data set is made publicly available to the community.11

Keywords: Gas sensor array, MOX sensor, Flow modulation, Early detection, Biomimetics, Sniffing

Fonollosa, J., Sheik, S., Huerta, R., Marco, S., (2015). Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring Sensors and Actuators B: Chemical 215, 618-629

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing

Fernandez, L., Marco, S., Gutierrez-Galvez, A., (2015). Robustness to sensor damage of a highly redundant gas sensor array Sensors and Actuators B: Chemical 218, 296-302

Abstract In this paper we study the role of redundant sensory information to prevent the performance degradation of a chemical sensor array for different distributions of sensor failures across sensor types. The large amount of sensing conditions with two different types of redundancy provided by our sensor array makes possible a comprehensive experimental study. Particularly, our sensor array is composed of 8 different types of commercial MOX sensors modulated in temperature with two redundancy levels: (1) 12 replicates of each sensor type for a total of 96 sensors and (2) measurements using 16 load resistors per sensors for a total of 1536 redundant measures per second. We perform two experiments to determine the performance degradation of the array with increasing number of damaged sensors in two different scenarios of sensor faults distributions across sensor types. In the first experiment, we characterize the diversity and redundancy of the array for increasing number of damaged sensors. To measure diversity and redundancy, we proposed a functional definition based on clustering of sensor features. The second experiment is devoted to determine the performance degradation of the array for the effect of faulty sensors. To this end, the system is trained to separate ethanol, acetone and butanone at different concentrations using a PCA–LDA model. Test set samples are corrupted by means of three different simulated types of faults. To evaluate the performance of the array we used the Fisher score as a measure of odour separability. Our results show that to exploit to the utmost the redundancy of the sensor array faulty sensory units have to be distributed uniformly across the different sensor types.

Keywords: Gas sensor arrays, Sensor redundancy, Sensor diversity, Sensor faults aging, Sensor damage, MOX sensors, Large sensor arrays

Fonollosa, Jordi, Neftci, Emre, Rabinovich, Mikhail, (2015). Learning of chunking sequences in cognition and behavior Plos Computational Biology PLoS Computational Biology , 11, (11), e1004592

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson’s disease and Schizophrenia.

Maynou, Joan, Pairo, Erola, Marco, Santiago, Perera, Alexandre, (2015). Sequence information gain based motif analysis BMC Bioinformatics 16, (1), 377

BACKGROUND:The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions.RESULTS:This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70 % of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions.CONCLUSIONS:Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at

Fonollosa, J., Neftci, E., Huerta, R., Marco, S., (2015). Evaluation of calibration transfer strategies between Metal Oxide gas sensor arrays Procedia Engineering EUROSENSORS 2015 , Elsevier (Freiburg, Germany) 120, 261-264

Abstract Inherent variability of chemical sensors makes necessary individual calibration of chemical detection systems. This shortcoming has traditionally limited usability of systems based on Metal Oxide (MOX) sensor arrays and prevented mass-production for some applications. Here, aiming at exploring transfer calibration between electronic nose systems, we exposed five identical 8-sensor detection units to controlled gas conditions. Our results show that a calibration model provides more accurate predictions when the tested board is included in the calibration dataset. However, we show that previously built calibration models can be extended to other units using a reduced number of measurements. While baseline correction seems imperative for successful baseline correction, among the different tested strategies, piecewise direct standardization provides more accurate predictions.

Keywords: Electronic nose, Calibration, MOX sensor, Machine Olfaction

Oller-Moreno, S., Singla-Buxarrais, G., Jiménez-Soto, J. M., Pardo, Antonio, Garrido-Delgado, R., Arce, L., Marco, Santiago, (2015). Sliding window multi-curve resolution: Application to gas chromatography - Ion Mobility Spectrometry Sensors and Actuators B: Chemical 15th International Meeting on Chemical Sensors , Elsevier (Buenos Aires, Argentina) 217, 13-21

Abstract Blind Source Separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on Ion Mobility Spectrometry, being Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and Multivariate Curve Resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography - ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions.

Keywords: Blind Source Separation, Multivariate Curve Resolution, Ion Mobility Spectrometry, Gas Chromatography, Hyphenated instrumentation, SIMPLISMA, co-elution

Palleja, T., Balsa, R., Tresanchez, M., Moreno, J., Teixido, M., Font, D., Marco, S., Pomareda, V., Palacin, J., (2014). Corridor gas-leak localization using a mobile Robot with a photo ionization detector sensor Sensor Letters 12, (6-7), 974-977

The use of an autonomous mobile robot to locate gas-leaks and air quality monitoring in indoor environments are promising tasks that will avoid risky human operations. However, these are challenging tasks due to the chaotic gas profile propagation originated by uncontrolled air flows. This paper proposes the localization of an acetone gas-leak in a 44 m-length indoor corridor with a mobile robot equipped with a PID sensor. This paper assesses the influence of the mobile robot velocity and the relative height of the PID sensor in the profile of the measurements. The results show weak influence of the robot velocity and strong influence of the relative height of the PID sensor. An estimate of the gas-leak location is also performed by computing the center of mass of the highest gas concentrations.

Keywords: Gas source detection, LIDAR sensor, Mobile robot, PID sensor, SLAM, Acetone, Air quality, Gases, Indoor air pollution, Mobile robots, Robots, Air quality monitoring, Autonomous Mobile Robot, Gas sources, Indoor environment, Leak localization, LIDAR sensors, Profile propagation, SLAM, Ionization of gases

Fonollosa, Jordi, Vergara, Alexander, Huerta, R., Marco, Santiago, (2014). Estimation of the limit of detection using information theory measures Analytica Chimica Acta 810, 1-9

Abstract Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.

Keywords: Limit of detection, Information theory, Mutual information, Heteroscedasticity, False positive/negative errors, Gas discrimination and quantification

Fresco-Cala, B., Jimenez-Soto, J. M., Cardenas, S., Valcarcel, M., (2014). Single-walled carbon nanohorns immobilized on a microporous hollow polypropylene fiber as a sorbent for the extraction of volatile organic compounds from water samples Microchimica Acta 181, (9-10), 1117-1124

We have evaluated the behavior of single-walled carbon nanohorns as a sorbent for headspace and direct immersion (micro)solid phase extraction using volatile organic compounds (VOCs) as model analytes. The conical carbon nanohorns were first oxidized in order to increase their solubility in water and organic solvents. A microporous hollow polypropylene fiber served as a mechanical support that provides a high surface area for nanoparticle retention. The extraction unit was directly placed in the liquid sample or the headspace of an aqueous standard or a water sample to extract and preconcentrate the VOCs. The variables affecting extraction have been optimized. The VOCs were then identified and quantified by GC/MS. We conclude that direct immersion of the fiber is the most adequate method for the extraction of VOCs from both liquid samples and headspace. Detection limits range from 3.5 to 4.3 ng L-1 (excepted for toluene with 25 ng L-1), and the precision (expressed as relative standard deviation) is between 3.9 and 9.6 %. The method was applied to the determination of toluene, ethylbenzene, various xylene isomers and styrene in bottled, river and tap waters, and the respective average recoveries of spiked samples are 95.6, 98.2 and 86.0 %.

Keywords: (Micro)solid phase extraction, Nanotechnology, Oxidized single-walled carbon nanohorns, Volatiles compounds, Waters

Marco, Santiago, (2014). The need for external validation in machine olfaction: emphasis on health-related applications Analytical and Bioanalytical Chemistry Springer Berlin Heidelberg 406, (16), 3941-3956

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

Keywords: Chemical sensor arrays, Pattern recognition, Chemometrics, Electronic noses, Robustness, Signal and data processing

Polese, Davide, Martinelli, Eugenio, Marco, Santiago, Di Natale, Corrado, Gutierrez-Galvez, Agustin, (2014). Understanding odor information segregation in the olfactory bulb by means of mitral and tufted cells PLoS ONE 9, (10), e109716

Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. To this end, we built a detailed neural model of the glomerular layer based on its known anatomical connections and conducted two simulated odor experiments. In the first experiment, the model was exposed to an odor stimulus dataset composed of six different odorants, each one dosed at six different concentrations. In the second experiment, we conducted an odor morphing experiment where a sequence of binary mixtures going from one odor to another through intermediate mixtures was presented to the model. The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space. Additionally, Fisher's discriminant ratio and Pearson's correlation coefficient were used to quantify odor identity and odor concentration information respectively. Our results showed that the architecture of the glomerular layer was able to mediate the segregation of odor information obtaining output spiking sequences of the principal neurons, namely the mitral and external tufted cells, strongly correlated with odor identity and concentration, respectively. An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

Martinez, Dani, Teixidó, Mercè, Font, Davinia, Moreno, Javier, Tresanchez, Marcel, Marco, Santiago, Palacín, Jordi, (2014). Ambient intelligence application based on environmental measurements performed with an assistant mobile robot Sensors 14, (4), 6045-6055

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.

Keywords: Ambient intelligence, Human thermal comfort, Robotic exploration

Bennetts, Victor, Schaffernicht, Erik, Pomareda, Victor, Lilienthal, Achim, Marco, Santiago, Trincavelli, Marco, (2014). Combining non selective gas sensors on a mobile robot for identification and mapping of multiple chemical compounds Sensors 14, (9), 17331-17352

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

Keywords: Environmental monitoring, Gas discrimination, Gas distribution mapping, Service robots, Open sampling systems, PID, Metal oxide sensors

Marco, S., Gutiérrez-Gálvez, A., Lansner, A., Martinez, D., Rospars, J. P., Beccherelli, R., Perera, A., Pearce, T. C., Verschure, P. F. M. J., Persaud, K., (2014). A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation Microsystem Technologies 20, (4-5), 729-742

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, in a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy and efficient combinatorial coding, with unmatched chemical information processing mechanisms. The last decade has seen important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. The EU-funded Project NEUROCHEM (Bio-ICT-FET- 216916) developed novel computing paradigms and biologically motivated artefacts for chemical sensing, taking its inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built that features a very large-scale sensor array (65,536 elements) using conducting polymer technology which mimics the olfactory receptor neuron layer. It implements derived computational neuroscience algorithms in an embedded system that interfaces the chemical sensors and processes their signals in real-time. This embedded system integrates abstracted computational models of the main anatomic building blocks in the olfactory pathway: the olfactory bulb, and olfactory cortex in vertebrates (respectively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor, an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions implemented in software. Finally, the algorithmic models are tested in mixed chemical plumes with an odour robot having navigation capabilities.

Oller-Moreno, S., Pardo, A., Jimenez-Soto, J. M., Samitier, J., Marco, S., (2014). Adaptive Asymmetric Least Squares baseline estimation for analytical instruments SSD 2014 Proceedings 11th International Multi-Conference on Systems, Signals & Devices (SSD) , IEEE (Castelldefels-Barcelona, Spain) , 1569846703

Automated signal processing in analytical instrumentation is today required for the analysis of highly complex biomedical samples. Baseline estimation techniques are often used to correct long term instrument contamination or degradation. They are essential for accurate peak area integration. Some methods approach the baseline estimation iteratively, trying to ignore peaks which do not belong to the baseline. The proposed method in this work consists of a modification of the Asymmetric Least Squares (ALS) baseline removal technique developed by Eilers and Boelens. The ALS technique suffers from bias in the presence of intense peaks (in relation to the noise level). This is typical of diverse instrumental techniques such as Gas Chromatography-Mass Spectrometry (GC-MS) or Gas Chromatography-Ion Mobility Spectrometry (GC-IMS). In this work, we propose a modification (named psalsa) to the asymmetry weights of the original ALS method in order to better reject large peaks above the baseline. Our method will be compared to several versions of the ALS algorithm using synthetic and real GC signals. Results show that our proposal improves previous versions being more robust to parameter variations and providing more accurate peak areas.

Keywords: Gas chromatography, Instruments, Radioactivity measurement, Signal processing, Analytical instrument, Analytical Instrumentation, Asymmetric least squares, Baseline estimation, Baseline removal, Gas chromatography-mass spectrometries (GC-MS), Instrumental techniques, Noise levels, Iterative methods

Fernandez, L., Marco, S., (2014). Calibration transfer between e-noses Signal Processing and Communications Applications Conference (SIU) Signal Processing and Communications Applications Conference (SIU), 2014 22nd , IEEE (Trabzon, Turkey) , 650-653

Electronic nose is an instrument which is composed of gas sensor array and pattern recognition unit. It is generally used for classifying, identifying or quantifying the odors or volatile organic components for these commonly used devices, calibration transfer is an important issue because of differences in each instrument, sensor drift, changes in environmental conditions or background changes. Calibration transfer is a transfer of model between different instruments which have different conditions. In this study, calibration transfer is applied to the e-noses which have different temperature conditions. Also the results of the direct standardization, piecewise direct standardization and orthogonal signal correction which are different calibration methods were compared. The results of the piecewise direct standardization method are more successful than the other methods for the dataset which is used in this study.

Keywords: Calibration, Conferences, Electronic noses, Ethanol, Instruments, Signal processing, Standardization

Sheik, S., Marco, S., Huerta, R., Fonollosa, J., (2014). Continuous prediction in chemoresisitive gas sensors using reservoir computing Procedia Engineering 28th European Conference on Solid-State Transducers (EUROSENSORS 2014) , Eurosensors (Brescia, Italy) 87, 843-846

Although Metal Oxide (MOX) sensors are predominant choices to perform fundamental tasks of chemical detection, their use has been mainly limited to relatively controlled scenarios where a gas sensor array is first exposed to a reference, then to the gas sample, and finally to the reference again to recover the initial state. In this paper we propose the use of MOX sensors along with Reservoir Computing algorithms to identify chemicals of interest. Our approach allows continuous gas monitoring in simple experimental setups without the requirement of acquiring recovery transient of the sensors, thereby making the system specifically suitable for online monitoring applications.

Keywords: Chemical sensing, Reservoir computing, Gas sensors, Dynamic gas mixtures, Electronic nose

Martínez, D., Moreno, J., Tresanchez, M., Teixidó, M., Font, D., Pardo, A., Marco, S., Palacín, J., (2014). Experimental application of an autonomous mobile robot for gas leak detection in indoor environments 17th International Conference on Information Fusion (FUSION) , IEEE (Salamanca, Spain) , 1-6

This paper presents the experimental application of an autonomous mobile robot for gas leak detection in indoor environments. The application is focused to automatize a human-risky operation in indoor areas. The goal of the autonomous mobile robot is the localization of a toxic gas leak source. So, the mobile robot has to explore the whole area and perform an auto-localization procedure based on a SLAM method and a LIDAR sensor. The mobile robot measures gas concentration by using a photoionization detector. The experimentation was realized in a large indoor environment in a university facility with a simulated gas leak source. The combination of the results from the auto-localization procedure with the information of the sensors allows the estimation of the gas leak source location.

Martínez, D., Moreno, J., Tresanchez, M., Teixidó, M., Palací, J., Marco, S., (2014). Preliminary results on measuring gas and wind intensity with a mobile robot in an indoor area ETFA 2014 19th IEEE International Conference on Emerging Technologies and Factory Automation , IEEE (Barcelona, Spain) , 1-5

This paper presents the preliminary results obtained when using a mobile robot to measure gas and wind intensity in an indoor area by means of several attached sensors such as a LIDAR, an e-nose, and an anemometer. The robot navigation was performed by means of a random path planning and the robot self location was performed by means of an SLAM procedure. This paper presents the first preliminary results obtained in a set of measurement experiments. In all cases, the indoor area has a fixed artificial simulated airflow and an induced gas leak source placed in different locations of the experimentation area. Results have shown different gas diffusion profiles in the different experiments performed.

Fernandez, L., Gutierrez-Galvez, A., Marco, S., (2014). Robustness to sensor damage of a highly redundant gas sensor array Procedia Engineering 28th European Conference on Solid-State Transducers (EUROSENSORS 2014) , Eurosensors (Brescia, Italy) 87, 851-854

Abstract In this paper we study the role of redundant sensory information to prevent the performance degradation of a chemical sensor array as the number of faulty sensors increases. The large amount of sensing conditions with two different types of redundancy provided by our sensor array makes possible a comprehensive experimental study. Particularly, our sensor array is composed of 8 different types of commercial MOX sensors modulated in temperature with two redundancy levels: 1) 12 replicates of each sensor type for a total of 96 sensors, and 2) measurements using 16 load resistors per sensors for a total of 1536 redundant measures per second. The system is trained to identify ethanol, acetone and butanone using a PCA-LDA model. Test set samples are corrupted by means of three different simulated types of faults. To evaluate the tolerance of the array against sensor failure, the Fisher Score is used as a figure of merit for the corrupted test set samples projected on the PCA-LDA model.

Keywords: Gas ensor arrays, sensor redundancy, MOX sensors, large sensor arrays.

Martínez, Dani, Pallejà, T., Moreno, Javier, Tresanchez, Marcel, Teixidó, M., Font, Davinia, Pardo, Antonio, Marco, Santiago, Palacín, Jordi, (2014). A mobile robot agent for gas leak source detection Advances in Intelligent Systems and Computing Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection (ed. Bajo Perez, Javier, Corchado Rodríguez, Juan M., Mathieu, Philippe, Campbell, Andrew, Ortega, Alfonso, Adam, Emmanuel, Navarro, Elena M., Ahrndt, Sebastian, Moreno, Maríaa N., Julián, Vicente), Springer International Publishing 293, 19-25

This paper presents an autonomous agent for gas leak source detection. The main objective of the robot is to estimate the localization of the gas leak source in an indoor environment without any human intervention. The agent implements an SLAM procedure to scan and map the indoor area. The mobile robot samples gas concentrations with a gas and a wind sensor in order to estimate the source of the gas leak. The mobile robot agent will use the information obtained from the onboard sensors in order to define an efficient scanning path. This paper describes the measurement results obtained in a long corridor with a gas leak source placed close to a wall.

Keywords: Gas detection, Mobile robot agent, Laser sensor, Self-localization

Karpas, Z., Guamán, A. V., Pardo, A., Marco, S., (2013). Comparison of the performance of three ion mobility spectrometers for measurement of biogenic amines Analytica Chimica Acta 758, (3), 122-129

The performance of three different types of ion mobility spectrometer (IMS) devices: GDA2 with a radioactive ion source (Airsense, Germany), UV-IMS with a photo-ionization source (G.A.S. Germany) and VG-Test with a corona discharge source (3QBD, Israel) was studied. The gas-phase ion chemistry in the IMS devices affected the species formed and their measured reduced mobility values. The sensitivity and limit of detection for trimethylamine (TMA), putrescine and cadaverine were compared by continuous monitoring of a stream of air with a given concentration of the analyte and by measurement of headspace vapors of TMA in a sealed vial. Preprocessing of the mobility spectra and the effectiveness of multivariate curve resolution techniques (MCR-LASSO) improved the accuracy of the measurements by correcting baseline effects and adjusting for variations in drift time as well as enhancing the signal to noise ratio and deconvolution of the complex data matrix to their pure components. The limit of detection for measurement of the biogenic amines by the three IMS devices was between 0.1 and 1.2 ppm (for TMA with the VG-Test and GDA, respectively) and between 0.2 and 0.7 ppm for putrescine and cadaverine with all three devices. Considering the uncertainty in the LOD determination there is almost no statistically significant difference between the three devices although they differ in their operating temperature, ionization method, drift tube design and dopant chemistry. This finding may have general implications on the achievable performance of classic IMS devices.

Keywords: Biogenic amines, Comparison of performance, Ion mobility spectrometry, Sensitivity, Signal processing, Vapor concentration

Pomareda, Victor, Lopez-Vidal, Silvia, Calvo, Daniel, Pardo, Antonio, Marco, Santiago, (2013). A novel differential mobility analyzer as VOCs detector and multivariate techniques for identification and quantification Analyst 138, (12), 3512-3521

A Differential Mobility Analyser (DMA) is a specific configuration of an Ion Mobility Spectrometer (IMS) where ions with different electrical mobilities are separated in space, instead of in time of drift, as in classical drift-time IMS. This work presents an instrument developed by the company Ioner, a parallel plate DMA instrument, but with crucial differences in the sheath flow and detection system when compared to other instruments in the market. These differences improve the resolving powers and sensitivities of the instrument. Additionally, datasets from IMS or DMA instruments are typically processed with univariate techniques when only qualitative detection is of interest. However, good performance in quantitative measurements can be achieved using multivariate data processing. This work presents for the first time, measurements with a stand alone DMA instrument and the multivariate data processing related for VOCs and environmental interesting samples.

Ziyatdinov, A., Diaz, E. Fernández, Chaudry, A., Marco, S., Persaud, K., Perera, A., (2013). A software tool for large-scale synthetic experiments based on polymeric sensor arrays Sensors and Actuators B: Chemical 177, 596-604

This manuscript introduces a software tool that allows for the design of synthetic experiments in machine olfaction. The proposed software package includes both, a virtual sensor array that reproduces the diversity and response of a polymer array and tools for data generation. The synthetic array of sensors allows for the generation of chemosensor data with a variety of characteristics: unlimited number of sensors, support of multicomponent gas mixtures and full parametric control of the noise in the system. The artificial sensor array is inspired from a reference database of seventeen polymeric sensors with concentration profiles for three analytes. The main features in the sensor data, like sensitivity, diversity, drift and sensor noise, are captured by a set of models under simplified assumptions. The generator of sensor signals can be used in applications related to test and benchmarking of signal processing methods, neuromorphic simulations in machine olfaction and educational tools. The software is implemented in R language and can be freely accessed.

Keywords: Gas Sensor Array, Conducting Polymers, Electronic Nose, Sensor Simulation, Synthetic Dataset, Benchmark, Educational Tool

Fonollosa, Jordi, Fernérndez, Luis, Huerta, Ramón, Gutiérrez-Gálvez, Agustín, Marco, Santiago, (2013). Temperature optimization of metal oxide sensor arrays using Mutual Information Sensors and Actuators B: Chemical Elsevier 187, (0), 331-339

The sensitivity and selectivity of metal oxide (MOX) gas sensors change significantly when the sensors operate at different temperatures. While previous investigations have presented systematic approaches to optimize the operating temperature of a single MOX sensor, in this paper we present a methodology to select the optimal operating temperature of all the MOX sensors constituent of a gas sensor array based on the multivariate response of all the sensing elements. Our approach estimates a widely used Information Theory measure, the so-called Mutual Information (MI), which quantifies the amount of information that the state of one random variable (response of the gas sensor array) can provide from the state of another random variable representing the gas quality. More specifically, our methodology builds sensor models from experimental data to solve the technical problem of populating the joint probability distribution for the MI estimation. We demonstrate the relevance of our approach by maximizing the MI and selecting the best operating temperatures of a four-sensor array sampled at 94 different temperatures to optimize the discrimination task of ethanol, acetic acid, 2-butanone, and acetone. In addition to being applicable in principle to sensor arrays of any size, our approach gives precise information on the ability of the system to discriminate odors according to the temperature of the MOX sensors, for either the optimal set of temperatures or the temperatures that may render inefficient operation of the system itself.

Keywords: MOX gas sensor, Temperature optimization, Limit of detection, Mutual Information, E-nose, Sensor array, Information Theory, Chemical sensing

Marco, S., Gutiérrez-Gálvez, A., Lansner, A., Martinez, D., Rospars, J. P., Beccherelli, R., Perera, A., Pearce, T., Vershure, P., Persaud, K., (2013). Biologically inspired large scale chemical sensor arrays and embedded data processing Proceedings of SPIE - The International Society for Optical Engineering Smart Sensors, Actuators, and MEMS VI , SPIE Digital Library (Grenoble, France) 8763, 1-15

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. EU Funded Project NEUROCHEM (Bio-ICT-FET- 216916) has developed novel computing paradigms and biologically motivated artefacts for chemical sensing taking inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built featuring a large scale sensor array (65K elements) in conducting polymer technology mimicking the olfactory receptor neuron layer, and abstracted biomimetic algorithms have been implemented in an embedded system that interfaces the chemical sensors. The embedded system integrates computational models of the main anatomic building blocks in the olfactory pathway: The olfactory bulb, and olfactory cortex in vertebrates (alternatively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions. Finally, the algorithmic models are tested with an odour robot with navigation capabilities in mixed chemical plumes.

Keywords: Antennal lobes, Artificial olfaction, Computational neuroscience, Olfactory bulbs, Plume tracking, Abstracting, Actuators, Algorithms, Biomimetic processes, Chemical sensors, Conducting polymers, Data processing, Flavors, Odors, Robots, Smart sensors, Embedded systems

Santano-Martínez, R., Leiva-González, R., Avazbeigi, M., Gutiérrez-Gálvez, A., Marco, S., (2013). Identification of molecular properties coding areas in rat's olfactory bulb by rank products Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing BIOSIGNALS 2013 , SciTePress (Barcelona, Spain) , 383-387

Neural coding of chemical information is still under strong debate. It is clear that, in vertebrates, neural representation in the olfactory bulb is a key for understanding a putative odour code. To explore this code, in this work we have studied a public dataset of radio images of 2-Deoxyglucose uptake (2-DG) in the olfactory bulb of rats in response to diverse odorants using univariate pixel selection algorithms: rank-products and Mann-Whitney U (MWU) test. Initial results indicate that some chemical properties of odorants preferentially activate certain areas of the rat olfactory bulb. While non-parametric test (MWU) has difficulties to detect these regions, rank-product provides a higher power of detection.

Keywords: 2-Deoxyglucose uptake, Chemotopy, Feature selection, Odour coding, Olfaction, Olfactory bulb

Fernandez, L., Gutierrez-Galvez, A., Marco, S., (2013). Multi-way analysis of diversity and redundancy factors in large MOX gas sensor data Metal Oxide-based Sensors 14th International Meeting on Chemical Sensors - IMCS 2012 , AMA Science Portal (Nuremberg, Germany) P2.07, 1279-1280

We propose the use of multi-way methods to analyze the contribution of diversity and redundancy to odor identification and concentration estimation in a large chemical sensor array. We use a chemical sensing system based on a large array of metal oxide sensors (MOX) and inspired on the diversity and redundancy of the olfactory epithelium. In order to analyze the role of diversity (different sensor type and temperature modulation) and redundancy (replicates of sensors and different load resistors) in odor quantification and discrimination tasks, we have acquired two datasets and modeled the data using multi-way techniques.

Keywords: Artificial Olfaction, Large array, MOX gas sensor, Multi-way methods

Hernandez Bennetts, V. M., Lilienthal, A. J., Khaliq, A. A., Pomareda Sese, V., Trincavelli, M., (2013). Towards real-world gas distribution mapping and leak localization using a mobile robot with 3d and remote gas sensing capabilities 2013 IEEE International Conference on Robotics and Automation (ICRA) (ed. Parker, Lynne E.), IEEE (Karlsruhe, Germany) , 2335-2340

Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.

Keywords: Laser beams, Measurement by laser beam, Mobile robots, Robot kinematics, Robot sensing systems

Gutiérrez-Gálvez, A., Marco, S., (2013). Study of the coding efficiency of populations of olfactory receptor neurons and olfactory glomeruli Frontiers in Neuroengineering Series Neuromorphic Olfaction (ed. Persaud, K. , Marco, S., Gutiérrez-Gálvez, A.), CRC Press (London, UK) , 59-82

Persaud, K. , Marco, S., Gutiérrez-Gálvez, A., (2013). Neuromorphic Olfaction Frontiers in Neuroengineering Series Neuromorphic Olfaction , CRC Press (London, UK)


  • Gas chromatograph/mass spectrometer (Thermoscientific) with robotic head-space sampler
  • 2 Infusion pumps K-systems
  • 6 channel vapor generator plus humidity control (Owlstone, UK)
  • Ion Mobility Spectrometer: Gas Detector Array (Airsense Analytics GmbH)
  • Computing and General Purpose Electronic Instrumentation
  • Field Asymmetric Ion Mobility Spectrometer (Owlstone, UK)
  • Corona Discharge Ion Mobility Spectrometer (3QBD, Israel)
  • Ultraviolet Ion Mobility Spectrometer (Gas Dortmund, Germany)


  • Dr. Lourdes Arce
    Dept. Química Analítica, Universidad de Córdoba, Spain
  • Dr. Alexandre Perera
    Centre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, Barcelona, Spain
  • Prof. Ramon Huerta
    Biocircuits Lab, University of California in San Diego, USA
  • Prof. J. W. Gardner
    Microsensors and Bioelectronics Lab, Dept. of Electric and Electronic Engineering, University of Warwick, UK
  • Prof. Achim Lilienthal
    Mobile Robotics and Olfaction Lab, University of Örebro, Sweden
  • Dr. Ivan Montoliu
    Nestlé Institute of Health Sciences, Laussane, Switzerland
  • Dr. Jordi Palacín
    Robotics Lab, Universitat de Lleida, Spain
  • Dr. Cristina Castro
    Sensors Technology, BSH-Zaragoza, Spain

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